Convergence Analysis of Aggregation-Broadcast in LoRA-enabled Federated Learning

• URL: http://arxiv.org/abs/2508.01348v1
• Date: Sat, 02 Aug 2025 12:54:17 GMT
• Title: Convergence Analysis of Aggregation-Broadcast in LoRA-enabled Federated Learning
• Authors: Xin Chen, Shuaijun Chen, Omid Tavallaie, Nguyen Tran, Shuhuang Xiang, Albert Zomaya,
• Abstract summary: Federated Learning (FL) enables collaborative model training across decentralized data sources.<n>Low-Rank Adaptation (LoRA) has been introduced into FL as an efficient fine-tuning method.<n>How to aggregate LoRA-updated local models on the server remains a critical and understudied problem.
• Score: 4.947778455281166
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated Learning (FL) enables collaborative model training across decentralized data sources while preserving data privacy. However, the growing size of Machine Learning (ML) models poses communication and computation challenges in FL. Low-Rank Adaptation (LoRA) has recently been introduced into FL as an efficient fine-tuning method, reducing communication overhead by updating only a small number of trainable parameters. Despite its effectiveness, how to aggregate LoRA-updated local models on the server remains a critical and understudied problem. In this paper, we provide a unified convergence analysis for LoRA-based FL. We first categories the current aggregation method into two major type: Sum-Product (SP) and Product-Sum (PS). Then we formally define the Aggregation-Broadcast Operator (ABO) and derive a general convergence condition under mild assumptions. Furthermore, we present several sufficient conditions that guarantee convergence of the global model. These theoretical analyze offer a principled understanding of various aggregation strategies. Notably, we prove that the SP and PS aggregation methods both satisfy our convergence condition, but differ in their ability to achieve the optimal convergence rate. Extensive experiments on standard benchmarks validate our theoretical findings.

Related papers

• FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation [6.5370850242187855]
  Federated Learning (FL) facilitates the fine-tuning of Foundation Models (FMs) using distributed data sources.<n>Low-Rank Adaptation (LoRA) gaining popularity due to its low communication costs and strong performance.<n>Existing methods lack formal convergence guarantees due to parameter truncation and biased gradient updates.
  arXiv  Detail & Related papers  (2025-05-24T04:12:12Z)
• Efficient Federated Class-Incremental Learning of Pre-Trained Models via Task-agnostic Low-rank Residual Adaptation [22.454292668849035]
  Federated Task-agnostic Low-rank Residual Adaptation (Fed-TaLoRA)<n>We develop a novel residual weight update mechanism that ensures accurate knowledge consolidation with minimal overhead.<n>Our methodological innovations are attributed to three key strategies: task-agnostic adaptation, post-aggregation model calibration, and strategic placement of LoRA modules.
  arXiv  Detail & Related papers  (2025-05-18T09:19:13Z)
• SD-LoRA: Scalable Decoupled Low-Rank Adaptation for Class Incremental Learning [73.93639228235622]
  Continual Learning with foundation models has emerged as a promising paradigm to exploit abundant knowledge acquired during pre-training for tackling sequential tasks.<n>Existing prompt-based and Low-Rank Adaptation-based (LoRA-based) methods often require expanding a prompt/LoRA pool or retaining samples of previous tasks.<n>We propose Scalable Decoupled LoRA (SD-LoRA) for class incremental learning, which continually separates the learning of the magnitude and direction of LoRA components without rehearsal.
  arXiv  Detail & Related papers  (2025-01-22T20:00:41Z)
• FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation Models [5.1613368481802455]
  Low-Rank Adaptation (LoRA) is a popular technique for efficient fine-tuning of foundation models.<n>We propose Federated Exact LoRA, or FedEx-LoRA, which adds a residual error term to the pretrained frozen weight matrix.<n>Our approach achieves exact updates with minimal computational and communication overhead, preserving LoRA's efficiency.
  arXiv  Detail & Related papers  (2024-10-12T08:22:44Z)
• Randomized Asymmetric Chain of LoRA: The First Meaningful Theoretical Framework for Low-Rank Adaptation [58.288682735160585]
  Low-Rank Adaptation (LoRA) is a popular technique for finetuning models. LoRA often under performs when compared to full- parameter fine-tuning. We present a framework that rigorously analyzes the adaptation rates of LoRA methods.
  arXiv  Detail & Related papers  (2024-10-10T18:51:53Z)
• Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates [71.81037644563217]
  Synchronous federated learning (FL) is a popular paradigm for collaborative edge learning. As some of the devices may have limited computational resources and varying availability, FL latency is highly sensitive to stragglers. We propose straggler-aware layer-wise federated learning (SALF) that leverages the optimization procedure of NNs via backpropagation to update the global model in a layer-wise fashion.
  arXiv  Detail & Related papers  (2024-03-27T09:14:36Z)
• Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL [57.745700271150454]
  We study the sample complexity of reinforcement learning in Mean-Field Games (MFGs) with model-based function approximation. We introduce the Partial Model-Based Eluder Dimension (P-MBED), a more effective notion to characterize the model class complexity.
  arXiv  Detail & Related papers  (2024-02-08T14:54:47Z)
• Aggregation Weighting of Federated Learning via Generalization Bound Estimation [65.8630966842025]
  Federated Learning (FL) typically aggregates client model parameters using a weighting approach determined by sample proportions. We replace the aforementioned weighting method with a new strategy that considers the generalization bounds of each local model.
  arXiv  Detail & Related papers  (2023-11-10T08:50:28Z)
• Over-the-Air Federated Learning and Optimization [52.5188988624998]
  We focus on Federated learning (FL) via edge-the-air computation (AirComp) We describe the convergence of AirComp-based FedAvg (AirFedAvg) algorithms under both convex and non- convex settings. For different types of local updates that can be transmitted by edge devices (i.e., model, gradient, model difference), we reveal that transmitting in AirFedAvg may cause an aggregation error. In addition, we consider more practical signal processing schemes to improve the communication efficiency and extend the convergence analysis to different forms of model aggregation error caused by these signal processing schemes.
  arXiv  Detail & Related papers  (2023-10-16T05:49:28Z)
• Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction [22.567754688492414]
  Cross-device Federated Learning (FL) faces significant challenges where low-end clients that could potentially make unique contributions are excluded from training large models due to their resource bottlenecks. Recent research efforts have focused on model-heterogeneous FL, by extracting reduced-size models from the global model and applying them to local clients accordingly. This paper presents a unifying framework for heterogeneous FL algorithms with online model extraction and provides a general convergence analysis for the first time.
  arXiv  Detail & Related papers  (2023-10-12T19:07:58Z)
• Federated Learning Aggregation: New Robust Algorithms with Guarantees [63.96013144017572]
  Federated learning has been recently proposed for distributed model training at the edge. This paper presents a complete general mathematical convergence analysis to evaluate aggregation strategies in a federated learning framework. We derive novel aggregation algorithms which are able to modify their model architecture by differentiating client contributions according to the value of their losses.
  arXiv  Detail & Related papers  (2022-05-22T16:37:53Z)
• On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning [15.300983585090794]
  We present a unifying framework for heterogeneous FL algorithms with em arbitrary adaptive online model pruning. In particular, we prove that under certain sufficient conditions, these algorithms converge to a stationary point of standard FL for general smooth cost functions. We illuminate two key factors impacting convergence: pruning-induced noise and minimum coverage index.
  arXiv  Detail & Related papers  (2022-01-27T20:43:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.